Search Results (1,043)

Quantitative structure–activity/property relationship (QSAR/QSPR) is a well-established methodology widely used to model molecular properties based on structure and is applied in fields such as drug design and environmental protection. The knowledge and procedures developed and used in QSPR modelling will be applied to the validation of protein folding rate models. Understanding the protein folding process is considered one of the most important scientific topics, and identifying the fundamental factors responsible for protein folding has been the subject of intensive research over the past 30 years. Among the structural descriptors determining the protein folding rate, the length of the protein sequence, the content of regular secondary structures, and the average contact row distance between amino acids in the 3D structure are the most important. Comparative studies of different methods for predicting protein folding rates are occasionally published, and we conducted one such study. We found that the experimental data in literature databases and the data available online are inconsistent and scattered. This is partly due to differences in experimental data and protein sequence lengths, but more so due to the questionable quality of the models themselves. We observed very large deviations in the predictions of ln(k_f) by some of the analysed models implemented as web servers. The root mean square errors (RMSEs) of some of the analysed models in predicting ln(k_f) for a new external set of proteins are much larger than the RMSEs obtained for the same models on the training sets. External validation demonstrates that protein folding rate models available on web servers have accuracy for external protein sets comparable to that of a simple model based solely on the logarithm of protein chain length. This finding, which highlights the importance of external model validation as recommended by the OECD guidelines for QSAR validation, is fundamental and offers a new perspective for improving protein folding rate models by applying the knowledge and procedures used in the QSPR methodology. Full article

Eutrophication is a pervasive issue in Mediterranean reservoirs, where external nutrient inputs and internal sediment releases interact to impair water quality and ecological stability. This study assessed the trophic condition of the artificial lake Cuga in Sardinia (Italy), mainly used for irrigation and providing potable water, by integrating watershed nutrient load estimates, inter-lake transfers, and internal phosphorus release. Field campaigns between July 2022 and May 2023 provided bi-monthly measurements of physical, chemical, and biological parameters, complemented by GIS-based land cover analysis and export coefficient modeling to quantify spatial nutrient sources. Additional phosphorus inputs from water transfers with a nearby reservoir were calculated, while internal sediment release was estimated using a calibrated mass balance model. Results revealed high nutrient concentrations, with mean total phosphorus of 128 mg P m⁻³, chlorophyll a averaging 9.9 mg m⁻³, and Secchi depth below 1 m, classifying the reservoir as eutrophic to hypertrophic under OECD and Carlson indices. Spatial loads were dominated by agricultural areas, while inter-lake transfers and internal sediment release contributed substantially to the overall phosphorus budget. The predictive Vollenweider model closely matched the observed conditions, confirming the robustness of the combined approach. Maintaining good ecological status in Mediterranean reservoirs is essential for safeguarding human well-being, as eutrophication degrades drinking-water quality, increases treatment costs, and can promote toxin-producing algal blooms with direct implications for public health. These findings highlight the need for integrated management strategies addressing both external and internal nutrient sources to mitigate eutrophication in Mediterranean reservoirs, which affects the ecosystem functioning and the related human needs and well-being. Full article

The rapid transition to Industry 4.0/5.0 has widened the gap between graduates’ skill sets and labor market expectations; this study aimed to profile student competencies and align academic pathways with inclusive and adaptive AI-driven learning. A quantitative design was applied: an online survey of n = 126 students (engineering and economics, February–March 2025), expert evaluations from 5 faculty and 5 employers on a 5-point scale, framed by T-shaped competencies, 4C skills, and Bloom’s taxonomy. Analysis was performed in Python 3.11; future demand until 2035 was forecasted using ARIMA and Prophet models trained on publicly available labor market data (OECD, WEF, Eurostat 2015–2024); competency prioritization employed K-Means clustering and Random Forest models. Strengths included cooperation 4.2, critical thinking 3.9, communication 3.8, and creativity 3.6. Deficits were programming 2.8, project management 3.2, and solution development 3.2; employers rated programming at 2.5 (−0.7 compared to faculty). Forecast 2025–2035 showed growth in demand for programming +56% (3.2 → 5.0), data analytics +39% (3.6 → 5.0), project management +34% (3.2 → 4.3), digital literacy +30% (3.7 → 4.8), and critical thinking +15% (3.9 → 4.5). Clustering identified critical (programming, analytics, project management), supporting (creativity, communication, teamwork), and optional (narrow theoretical depth) competencies. Curriculum adjustment with practice-oriented modules, AI-enabled adaptive learning, and systematic university–employer feedback is essential; the proposed AI-supported profiling model is scalable and enhances inclusiveness. Full article

Cities are increasingly expected to address digital transformation and sustainability challenges at the same time. However, existing urban indices generally approach smart city and sustainable city perspectives separately, which limits their ability to capture the integrated nature of contemporary urban development. In addition, many index-based studies rely on similar methodological choices. This study develops a composite Sustainable Smart City (SSC) index supported by a systematic scoring framework that brings smartness and sustainability together. The proposed framework follows a step-by-step procedure covering data preparation, normalization, weighting, aggregation, and final scoring. To address information overlap among indicators, a Redundancy-Penalized Entropy Weighting (RPEW) approach is applied. Then, overall SSC scores are calculated using a soft non-compensatory aggregation to emphasize balanced performance across dimensions. The framework is empirically illustrated through a cross-country case study including 38 OECD (Organization for Economic Co-Operation and Development) countries. A machine-learning-based polynomial forecasting approach is used for a limited number of indicators to deal with data gaps allowing the assessment to reflect more up-to-date conditions. The results highlight clear differences in SSC performance and show that strong outcomes in a single dimension are not sufficient to achieve high overall SSC scores. Instead, balanced progress across economic, digital, environmental, governance, mobility, and social dimensions plays an important role. In addition, the proposed framework provides a practical basis for comparative analysis, benchmarking, and policy-oriented evaluation of smart and sustainable urban development. Full article

Transport is one of the few major sectors in Europe where greenhouse gas emissions have not declined despite tightening climate policy. Road transport remains dominated by fossil fuels, rising travel demand, and growing freight activity. This paper develops a multi-indicator benchmarking framework to assess the extent to which recent road-transport developments in EU-27 Member States align with structural expectations derived from 1.5 °C and 2 °C mitigation pathways. A multi-indicator framework is developed combining emissions and air-quality pressures, system drivers, and urban accessibility for 2019–2023, using harmonized Eurostat, European Environment Agency, WHO, and OECD data. The analysis follows a dual-track design. First, hierarchical agglomerative clustering identifies national transport–climate profiles. Second, PROMETHEE II is applied to generate an outranking-based performance index and country ranking. Five distinct clusters emerge, ranging from carbon-intensive, car-dependent systems with limited electrification and weak accessibility to “sustainability leaders” characterized by lower emissions, higher shares of low-emission vehicles, and strong public-transport accessibility. PROMETHEE results align with this typology: Nordic and north-western countries rank highest, while several southern and eastern countries show negative net flows linked to persistent car dependence, slower fleet transition, and higher pollution exposure. The results suggest that while several countries demonstrate structural progress toward transport decarbonization, none exhibit a performance profile fully consistent with transition patterns associated with 1.5 °C-aligned mitigation pathways. Full article

As the European population ages, the sustainability of pension systems faces a trilemma: the structural conflict between achieving benefit adequacy, fiscal stability, and labor market flexibility. This study investigates the primary research hypothesis that these three objectives involve trade-offs under current institutional designs. We examine the structural interrelationships between economic development, population health, and institutional pension characteristics across the EU’s 27 member states. Using cross-sectional data from Eurostat and the OECD from 2023, the study employs a multivariate framework, including Multiple Factor Analysis (MFA) and Principal Component Analysis (PCA), to visualize latent trade-offs. Non-parametric statistical tests were applied to validate structural differences between the Nordic, Continental, Southern, and Central and Eastern European (CEE) welfare regimes. The paper’s central argument is that pension sustainability is less a demographic inevitability and more a path-dependent result of institutional “exit cultures” and regional health-wealth traps. The analysis explains 56.7% of the total variance across two primary dimensions, revealing a persistent east–west divide where GDP per capita and Healthy Life Years (HLYs) at age 65 are strongly coupled. Additionally, the analysis identified a fundamental sustainability trade-off: countries with higher pension expenditures and replacement rates, such as those in the Southern and Continental clusters, have significantly earlier labor market exit ages. Statistical evidence shows that the gender pension gap is the most significant factor in differentiating welfare regimes, with the CEE region showing significantly lower inequality than the Western cluster. Ultimately, the findings contribute to public administration literature by demonstrating that policy interventions must prioritize addressing the culture of early retirement in Western countries and the health-wealth trap in Eastern countries to ensure long-term viability. Full article

While Environmental, Social, and Governance (ESG) criteria are extensively utilized for corporate evaluation, empirical evidence regarding sovereign ESG efficiency remains scarce. Existing national sustainability indices often fail to account for how effectively a nation translates its economic resources into ESG outcomes. This study proposes a two-stage Data Envelopment Analysis (DEA) framework to evaluate the efficiency of 38 OECD countries in 2020. The national production process is decomposed into two sequential phases: (1) Economic Efficiency, transforming resource inputs (labor and energy) into intermediate economic outputs (GDP and trade openness), and (2) ESG Transformation Efficiency, converting those intermediate outputs into a composite ESG score. A novel quartile-based classification scheme is further applied to categorize countries into strategic groups for benchmarking. Empirical results reveal significant heterogeneity across the OECD. Estonia, Iceland, and Latvia emerge as “Win–Win” benchmarks, demonstrating high efficiency in both economic production and ESG transformation. Conversely, the United States is classified as a “Laissez-faire” member, exhibiting low performance in both stages relative to its capacity. Additionally, second-stage regression analysis indicates that while higher income is negatively associated with ESG transformation efficiency, government effectiveness acts as a significant positive driver. This research contributes a transparent, reproducible framework for sovereign ESG analytics that relates outcomes directly to economic capacity. It provides policymakers with an interpretable benchmarking tool to identify national sustainability gaps and facilitates actionable insights for enhancing public-sector effectiveness in achieving ESG goals. Full article

Artificial intelligence is already being used in the administration of Justice, with various applications assisting judges in resolving cases. In particular, in criminal Justice, these applications include predictive Justice and decision-making assistance through the assessment of facts, as well as the classification of criminals into risk groups. This article examines the current regulatory and ethical framework (AI Act, Council of Europe Convention on AI, CEPEJ Ethical Charter, UNESCO and OECD principles) and develops a regulatory approach to the use of AI systems by judges and prosecutors. The methodology is based on a doctrinal analysis of international, EU, and professional ethical literature, as well as on a synthesis of principles of judicial conduct (Bangalore Principles, Magna Carta of Judges). To strike a balance between the rules of governing system use and judicial ethics, the article proposes a consistent framework of ethical principles (legitimacy, transparency, accountability, integrity, human oversight, prohibition of discrimination) and introduces a practical “line of reasoning” with key questions that judges should consider before and during the use of intelligent tools (risks, bias, proportionality, understanding of the algorithm, and impact on judicial judgment). The article concludes that AI may improve the efficiency of the justice system only when included inside a strong ethical framework and specialized training, guaranteeing that final judicial decisions remain solely human and fully aligned with the rule of law. Full article

(1) Background: Bio-based plastics are an alternative for commonly used petroleum-based plastics, and their production will increase in the coming decades. In this work, two innovative bio-based plastics, i.e., polylactide-based (PLA-based) and polyhydroxybutyrate-based (PHBV-based), were studied with regard to their effect on the growth of higher plants (Sorghum saccharatum, Sinapsis alba) in the soil environment. (2) Methods: The experiments were conducted in pots filled with the Organisation for Economic Co-operation and Development (OECD) reference soil with or without one of the bioplastics at concentrations from 0.1% w/w to 12.5% w/w. This study is one of few works in which soil instead of another medium (e.g., deionised water) was used for the evaluation of the impact of microplastics on plant growth. (3) Results: Mustard (Sinapsis alba) was more sensitive to the presence of microplastics in the soil than sorghum (Sorghum saccharatum). The length of mustard shoots exposed to PLA-based plastic were shorter from 25% to about 56% than those in the control tests, while in the case of PHBV-based plastic, the decrease of mustard shoot length varied from 6% to 26%. The presence of the bioplastics studied, in particular the PLA-based one, at the levels of 2.5% w/w and higher contributed to reduced germination and shoot length and to the decrease in the relative chlorophyll content. (4) Conclusions: These three endpoints occurred to be more sensitive than the dry weight or elemental composition of plant biomass. They are recommended to be used in the evaluation of phytotoxicity of microbioplastics to study how to maintain the sustainability of the soil environment. Full article

Sustainable competitiveness has increasingly become an important policy objective for OECD countries, as economic performance is expected to be balanced with environmental protection, social well-being, and effective governance structures. The aim of this study is to evaluate and compare the sustainable competitiveness performance of OECD countries from a holistic perspective. In the analysis, six criteria reflecting the main dimensions of global sustainable competitiveness were considered. Criterion weights were calculated using the CRITIC (Criteria Importance Through Intercriteria Correlation) method, an objective weighting technique that does not rely on subjective judgments. These weights were then integrated with the CoCoSo (Combined Compromise Solution) method to rank the sustainable competitiveness performance of countries. In the final stage, a clustering analysis was applied to group OECD countries exhibiting similar sustainability characteristics. The findings indicate that natural capital emerges as the most influential dimension within the evaluation framework. According to the ranking results, Finland, Sweden, Lithuania, Denmark, and Estonia are positioned among the countries with the highest sustainable competitiveness performance. The results reveal noticeable differences across OECD countries, demonstrating that environmental, social, economic, and governance-related dimensions affect country performance in distinct ways. A direct comparison with the 2025 Global Sustainable Competitiveness Index shows a strong but not perfect association between the two rankings (Spearman’s ρ = 0.977), indicating structural consistency alongside meaningful mid-ranking shifts. Furthermore, the clustering results enable the identification of country groups sharing relatively similar sustainability profiles. Overall, the study contributes methodologically to the sustainable competitiveness literature by integrating objective weighting, multi-criteria decision-making, and clustering analysis within a unified analytical framework, while also offering insights for comparative policy evaluation. Full article

Decoupling between CO₂ emissions and economic growth is critical to reversing climate change. The OECD plays a crucial role in this regard, given its considerable share of global CO₂ emissions and GDP. This study examines the decoupling performance and the roles of renewable energy transition, as well as specific eco-innovations on climate change mitigation and environmental technology development across the OECD economies. The preliminary tests on a large panel of OECD countries identify cross-sectional dependence, structural breaks and heterogeneity. For robustness, the study proposes Fourier-CS-ARDL, Fourier-AMG, and Fourier–Dumitrescu–Hurlin methods as generalizations of their linear counterparts. After identifying cointegration and its singularity with Fourier-bootstrapping bounds and Fourier–Johansen tests, the modeling stage suggested a positive, but significantly inelastic long- and short-run elasticity of emissions to economic growth. Most of these effects are reversed by renewable energy transition in the long run and partially reversed in the short run. These CO₂ mitigation effects are also evident across different eco-innovations with varying temporal impacts. Novel Fourier causality tests identify feedback loops between CO₂ and CO₂-mitigating factors, as well as unidirectional causality from growth to all mitigating factors, confirming the indirect effect of growth on CO₂ mitigation. Overall, these results clearly suggest “relative” decoupling in OECD accompanied by CO₂e mitigation effects from eco-innovations and energy transition, and highlight the potential for green growth following the successful adaptation of energy transition and eco-innovations. Policymakers in OECD are encouraged to leverage the identified feedback mechanisms and establish international technology transfer policies to homogenously curb CO₂ emissions. Full article

Objectives: This scoping review aimed to map European and Romanian pharmaceutical legislation and policy-related evidence and to examine how legislative harmonization translates into access outcomes in Romania. Eligibility criteria: Legislative documents, institutional reports, market analyses, and peer-reviewed studies addressing pharmaceutical regulation, pricing, reimbursement, and access to medicines (2000–2024). Sources of evidence: EUR-Lex, the Romanian Legislative Portal, PubMed, Scopus, Google Scholar, and institutional sources (European Commission, OECD, WHO, EFPIA, NAMMDR, CNAS). Charting methods: Data were extracted using a standardized charting form and synthesized narratively across thematic domains (regulatory harmonization, pricing and reimbursement, medicine shortages, comparative EU indicators, and health system implications). Results: Fifty sources were included. The mapped evidence consistently identified three dominant patterns: (1) prolonged time-to-availability for centrally authorized medicines, with mean delays exceeding 800 days in Romania compared with approximately 578 days at EU level; (2) limited availability of innovative therapies, particularly in oncology (approximately 20% availability in Romania versus around 50% EU average); and (3) recurrent medicine shortages associated with low-price regulation and parallel export dynamics. Evidence gaps include limited Romania-specific empirical evaluation of the causal effects of individual policy levers (e.g., external reference pricing, reimbursement timelines, clawback mechanisms). Conclusions: Legislative harmonization alone has not ensured equitable or timely access to medicines in Romania. The evidence suggests that national pricing, reimbursement, and supply governance mechanisms mediate the relationship between EU regulation and real-world patient access, highlighting the need for targeted policy reforms and further empirical investigation. Full article

This paper develops a pessimistic two-stage network data envelopment analysis (DEA) model that integrates interval-valued data and endogenous weight restrictions within a unified linear programming framework. The proposed approach explicitly captures internal network structures while addressing bounded data uncertainty through an interval-to-deterministic transformation that preserves linearity and avoids probabilistic assumptions. Robustness is interpreted in the pessimistic interval DEA sense, where efficiency is evaluated under worst-case realizations of observed bounds rather than through explicit uncertainty-set optimization. To mitigate weight degeneracy and enhance discrimination power, data-driven proportional weight restrictions are introduced; these endogenous bounds are constructed solely from observed data and regularize the multiplier space without relying on subjective preferences or tuning parameters, while maintaining scale invariance and the nonparametric nature of DEA. The model admits equivalent multiplier and envelopment formulations and enables meaningful decomposition of overall efficiency into stage-specific components. Fundamental theoretical properties—including feasibility, boundedness, monotonicity, efficiency decomposition, and special case consistency—are rigorously established. An empirical application to OECD macroeconomic data, accompanied by sensitivity evaluation, demonstrates the stability and discriminatory capability of the proposed framework under bounded variability. Computational analysis confirms that the model retains linear programming structure and exhibits linear growth in problem size with respect to the number of decision-making units, thereby preserving the scalability characteristics of classical two-stage network DEA formulations. The proposed framework provides a theoretically grounded and computationally tractable approach for network efficiency analysis under bounded interval uncertainty. Full article

Plastic pollution is a global challenge. Despite plastics being complex chemical mixtures, hazard research has focused on particulate forms and the risks of plastic additives, especially for environmental organisms, remain poorly understood. This is a significant knowledge gap considering ubiquitous organismal exposure to plastics and the associated 16,000+ additives. The aim of this study was to provide ecotoxicological characterization of aqueous eluates of foamed plastic consumer products and propose a test battery for toxicity screening. To achieve this, the hazard of eluates of six randomly selected foamed plastic products was evaluated using aquatic decomposers, autotrophs and heterotrophs (Vibrio fischeri, Raphidocelis subcapitata, Lemna minor, Thamnocephalus platyurus, Heterocypris incongruens, Daphnia magna). Alarmingly, all plastic eluates affected the organisms, though toxicity varied among materials and species. Results showed that short-term contact may underestimate plastic eluate toxicity. To increase the environmental relevance of hazard assessment of foamed plastic eluates, harmonizing leachate preparation, using natural water and avoiding (excessive) filtration of eluates should be considered. OECD/ISO assays with R. subcapitata, H. incongruens and D. magna (96 h) can be recommended as a minimal sensitive battery for effective screening of plastic eluate toxicity. Full article

Tax administrations are undergoing rapid digitalisation, while sustainability requirements are increasingly embedded in corporate governance frameworks. These parallel transformations are raising new expectations for transfer pricing (TP) documentation, which must be accurate, transparent, and audit-ready. This paper investigates the extent to which artificial intelligence (AI)—specifically natural language processing (NLP), robotic process automation (RPA), and machine-learning techniques—can support a sustainability-oriented governance framework for TP documentation in multinational enterprises. Using a longitudinal case study of the OMEGA Group, operating across 21 jurisdictions, we analyse an AI-enabled documentation architecture that streamlines data extraction, enhances comparability analysis, and strengthens audit preparedness, in line with the OECD Transfer Pricing Guidelines and relevant European Union regulatory requirements. The empirical evidence indicates substantial improvements in documentation efficiency (−68.3%), a significant reduction in processing errors (−81.5%), and higher audit acceptance rates (+27%). Beyond compliance, AI-driven digital workflows contribute to sustainability objectives by reducing resource consumption, improving data traceability, and facilitating alignment with CSRD-related reporting requirements. Overall, the findings demonstrate that AI-enabled TP documentation can evolve into a strategic pillar of sustainable tax governance, provided that its outputs remain explainable, auditable, and grounded in professional judgment. The study proposes an integrated governance framework that connects digital transformation, regulatory compliance, and sustainability within contemporary TP management practices. Full article